JMP user Peter Reich, a Regents Professor and Distinguished McKnight University Professor at the University of Minnesota, is featured in the December 2009 issue of Science magazine for his long-term experiment on biodiversity and global change. For more than a decade, Reich has used JMP to investigate the interactive effects of elevated levels of carbon dioxide (CO₂) and nitrogen (N) on plant species diversity. Read the customer success story to learn more about that experiment and the ways he analyzes scientific data with JMP.

Perhaps surprisingly, Reich found that although elevated CO₂ reduced diversity by 2 percent and N addition reduced diversity by 16 percent, in combination, N and CO₂ reduced diversity by only 8 percent.

“Despite concerns that rising CO₂ and N pollution levels together could lead to as much as 80 percent loss in biodiversity, our 10-year field experiment shows that the joint effects are likely to be much, much smaller,” says Reich in a Science Podcast interview.

He is quick to note that while this is good news for biodiversity, the findings don’t lessen the need to curb CO₂ emissions, given the other ways it impacts the planet.

Reich and his team conducted the study in synthetic grasslands communities in the Cedar Creek LTER site in Minnesota. Using the Free-Air CO₂ Enrichment Technique (FACE), they manipulated atmospheric conditions, exposing plant species to different combinations of natural or elevated levels of CO₂ and N. The elevated levels simulated future conditions and how ecosystems might react to them.

A similar study set in annual serpentine grassland at Jasper Ridge, CA, concludes that, in some settings, climate and atmospheric changes are simply additive combinations – rather than interactive combinations – of each effect by itself. So the question becomes whether or not the results of Reich’s experiment are globally representative.

If the CA experiment is more representative, then researchers can “take the knowledge of single global change factors studied one at a time and make models that predict what will happen when many things change at once, and have some confidence that many of these models would be okay,” Reich explains.

But more often, predictions made by studying one factor at a time can be inaccurate. With multiple factors (e.g., plants, soil microbes, insects), there is potential for interactions to occur. This makes it more difficult to come up with predictive models that can be applied to other plant communities.

Very few government-funded projects have conducted multi-factor experiments that look at the way global change factors such as CO₂, N or climate change influence grasslands, forests and other vegetation. And Reich’s study, now entering its 12th year, is one of the longest-term experiments of its kind.

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